Python-Based GeoImagery Dataset Development for Deep Learning-Driven Forest Wildfire Detection

In recent years, leveraging satellite imagery with deep learning ( DL ) architectures has become an effective approach for environmental monitoring tasks, including forest wildfire detection. Nevertheless, this integration requires substantial high-quality labeled data to train the DL models accurately. Leveraging the capabilities of multiple Python libraries, such as rasterio and GeoPandas, and Google Earth Engine’s Python API , this study introduces a streamlined methodology to efficiently gather, label, augment, process, and evaluate a large-scale bi-temporal high-resolution satellite imagery dataset for DL -driven forest wildfire detection. Known as the California Wildfire GeoImaging Dataset ( CWGID ), this dataset comprises over 100,000 labeled ’before’ and ’after’ wildfire image pairs, created from pre-existing satellite imagery. An analysis of the dataset using pre-trained and adapted Convolutional Neural Network ( CNN ) architectures, such as VGG16 and EfficientNet, achieved accuracies of respectively 76% and 93%. The pipeline outlined in this paper demonstrates how Python can be used to gather and process high-resolution satellite imagery datasets, leading to accurate wildfire detection and providing a tool for broader environmental monitoring.